



'''simple docstring'''

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def 				a  (     UpperCamelCase_    :    Union[str, Any]			)       ->	str:
		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts			)
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config			)
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights			)

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    Optional[Any]			)       ->	Union[str, Any]:
		if args.student_type == "roberta":
				snake_case__	     =False
		elif args.student_type == "gpt2":
				snake_case__	     =False






def 				a  (     UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    List[str]			)       ->	List[Any]:
		if args.student_type == "roberta":
				snake_case__	     =False






def 				a  (     )       ->	str:
		snake_case__	     =argparse.ArgumentParser(description='Training'			)
		parser.add_argument('--force'      ,					action='store_true'      ,					help='Overwrite dump_path if it already exists.'			)

		parser.add_argument(
		    '--dump_path'      ,					type=UpperCamelCase_      ,					required=UpperCamelCase_      ,					help='The output directory (log, checkpoints, parameters, etc.)'			)
		parser.add_argument(
		    '--data_file'      ,					type=UpperCamelCase_      ,					required=UpperCamelCase_      ,					help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'      ,					)

		parser.add_argument(
		    '--student_type'      ,					type=UpperCamelCase_      ,					choices=['distilbert', 'roberta', 'gpt2']      ,					required=UpperCamelCase_      ,					help='The student type (DistilBERT, RoBERTa).'      ,					)
		parser.add_argument('--student_config'      ,					type=UpperCamelCase_      ,					required=UpperCamelCase_      ,					help='Path to the student configuration.'			)
		parser.add_argument(
		    '--student_pretrained_weights'      ,					default=UpperCamelCase_      ,					type=UpperCamelCase_      ,					help='Load student initialization checkpoint.'			)

		parser.add_argument(
		    '--teacher_type'      ,					choices=['bert', 'roberta', 'gpt2']      ,					required=UpperCamelCase_      ,					help='Teacher type (BERT, RoBERTa).'			)
		parser.add_argument('--teacher_name'      ,					type=UpperCamelCase_      ,					required=UpperCamelCase_      ,					help='The teacher model.'			)

		parser.add_argument('--temperature'      ,					default=2.0      ,					type=UpperCamelCase_      ,					help='Temperature for the softmax temperature.'			)
		parser.add_argument(
		    '--alpha_ce'      ,					default=0.5      ,					type=UpperCamelCase_      ,					help='Linear weight for the distillation loss. Must be >=0.'			)
		parser.add_argument(
		    '--alpha_mlm'      ,					default=0.0      ,					type=UpperCamelCase_      ,					help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'      ,					)
		parser.add_argument('--alpha_clm'      ,					default=0.5      ,					type=UpperCamelCase_      ,					help='Linear weight for the CLM loss. Must be >=0.'			)
		parser.add_argument('--alpha_mse'      ,					default=0.0      ,					type=UpperCamelCase_      ,					help='Linear weight of the MSE loss. Must be >=0.'			)
		parser.add_argument(
		    '--alpha_cos'      ,					default=0.0      ,					type=UpperCamelCase_      ,					help='Linear weight of the cosine embedding loss. Must be >=0.'			)

		parser.add_argument(
		    '--mlm'      ,					action='store_true'      ,					help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'			)
		parser.add_argument(
		    '--mlm_mask_prop'      ,					default=0.1_5      ,					type=UpperCamelCase_      ,					help='Proportion of tokens for which we need to make a prediction.'      ,					)
		parser.add_argument('--word_mask'      ,					default=0.8      ,					type=UpperCamelCase_      ,					help='Proportion of tokens to mask out.'			)
		parser.add_argument('--word_keep'      ,					default=0.1      ,					type=UpperCamelCase_      ,					help='Proportion of tokens to keep.'			)
		parser.add_argument('--word_rand'      ,					default=0.1      ,					type=UpperCamelCase_      ,					help='Proportion of tokens to randomly replace.'			)
		parser.add_argument(
		    '--mlm_smoothing'      ,					default=0.7      ,					type=UpperCamelCase_      ,					help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'      ,					)
		parser.add_argument('--token_counts'      ,					type=UpperCamelCase_      ,					help='The token counts in the data_file for MLM.'			)

		parser.add_argument(
		    '--restrict_ce_to_mask'      ,					action='store_true'      ,					help='If true, compute the distillation loss only the [MLM] prediction distribution.'      ,					)
		parser.add_argument(
		    '--freeze_pos_embs'      ,					action='store_true'      ,					help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'      ,					)
		parser.add_argument(
		    '--freeze_token_type_embds'      ,					action='store_true'      ,					help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'      ,					)

		parser.add_argument('--n_epoch'      ,					type=UpperCamelCase_      ,					default=3      ,					help='Number of pass on the whole dataset.'			)
		parser.add_argument('--batch_size'      ,					type=UpperCamelCase_      ,					default=5      ,					help='Batch size (for each process).'			)
		parser.add_argument(
		    '--group_by_size'      ,					action='store_false'      ,					help='If true, group sequences that have similar length into the same batch. Default is true.'      ,					)

		parser.add_argument(
		    '--gradient_accumulation_steps'      ,					type=UpperCamelCase_      ,					default=50      ,					help='Gradient accumulation for larger training batches.'      ,					)
		parser.add_argument('--warmup_prop'      ,					default=0.0_5      ,					type=UpperCamelCase_      ,					help='Linear warmup proportion.'			)
		parser.add_argument('--weight_decay'      ,					default=0.0      ,					type=UpperCamelCase_      ,					help='Weight decay if we apply some.'			)
		parser.add_argument('--learning_rate'      ,					default=5e-4      ,					type=UpperCamelCase_      ,					help='The initial learning rate for Adam.'			)
		parser.add_argument('--adam_epsilon'      ,					default=1e-6      ,					type=UpperCamelCase_      ,					help='Epsilon for Adam optimizer.'			)
		parser.add_argument('--max_grad_norm'      ,					default=5.0      ,					type=UpperCamelCase_      ,					help='Max gradient norm.'			)
		parser.add_argument('--initializer_range'      ,					default=0.0_2      ,					type=UpperCamelCase_      ,					help='Random initialization range.'			)

		parser.add_argument(
		    '--fp16'      ,					action='store_true'      ,					help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'      ,					)
		parser.add_argument(
		    '--fp16_opt_level'      ,					type=UpperCamelCase_      ,					default='O1'      ,					help=(
		        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
		        'See details at https://nvidia.github.io/apex/amp.html'
		    )      ,					)
		parser.add_argument('--n_gpu'      ,					type=UpperCamelCase_      ,					default=1      ,					help='Number of GPUs in the node.'			)
		parser.add_argument('--local_rank'      ,					type=UpperCamelCase_      ,					default=-1      ,					help='Distributed training - Local rank'			)
		parser.add_argument('--seed'      ,					type=UpperCamelCase_      ,					default=56      ,					help='Random seed'			)

		parser.add_argument('--log_interval'      ,					type=UpperCamelCase_      ,					default=500      ,					help='Tensorboard logging interval.'			)
		parser.add_argument('--checkpoint_interval'      ,					type=UpperCamelCase_      ,					default=4000      ,					help='Checkpoint interval.'			)
		snake_case__	     =parser.parse_args()
		sanity_checks(UpperCamelCase_			)

		# ARGS #
		init_gpu_params(UpperCamelCase_			)
		set_seed(UpperCamelCase_			)
		if args.is_master:
				if os.path.exists(args.dump_path			):
						if not args.force:
								raise ValueError(
								    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
								    ' itUse `--force` if you want to overwrite it'			)
						else:
								shutil.rmtree(args.dump_path			)

				if not os.path.exists(args.dump_path			):
						os.makedirs(args.dump_path			)
				logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""			)

				# SAVE PARAMS #
				logger.info(f"""Param: {args}"""			)
				with open(os.path.join(args.dump_path      ,					'parameters.json'			)      ,					'w'			) as f:
						json.dump(vars(UpperCamelCase_			)      ,					UpperCamelCase_      ,					indent=4			)
				git_log(args.dump_path			)

		snake_case__     ,	snake_case__     ,	snake_case__	     =MODEL_CLASSES[args.student_type]
		snake_case__     ,	snake_case__     ,	snake_case__	     =MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		snake_case__	     =teacher_tokenizer_class.from_pretrained(args.teacher_name			)
		snake_case__	     ={}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				snake_case__	     =tokenizer.all_special_tokens.index(UpperCamelCase_			)
				snake_case__	     =tokenizer.all_special_ids[idx]
		logger.info(f"""Special tokens {special_tok_ids}"""			)
		snake_case__	     =special_tok_ids
		snake_case__	     =tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(f"""Loading data from {args.data_file}"""			)
		with open(args.data_file      ,					'rb'			) as fp:
				snake_case__	     =pickle.load(UpperCamelCase_			)

		if args.mlm:
				logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""			)
				with open(args.token_counts      ,					'rb'			) as fp:
						snake_case__	     =pickle.load(UpperCamelCase_			)

				snake_case__	     =np.maximum(UpperCamelCase_      ,					1			) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						snake_case__	     =0.0  # do not predict special tokens
				snake_case__	     =torch.from_numpy(UpperCamelCase_			)
		else:
				snake_case__	     =None

		snake_case__	     =LmSeqsDataset(params=UpperCamelCase_      ,					data=UpperCamelCase_			)
		logger.info('Data loader created.'			)

		# STUDENT #
		logger.info(f"""Loading student config from {args.student_config}"""			)
		snake_case__	     =student_config_class.from_pretrained(args.student_config			)
		snake_case__	     =True

		if args.student_pretrained_weights is not None:
				logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""			)
				snake_case__	     =student_model_class.from_pretrained(args.student_pretrained_weights      ,					config=UpperCamelCase_			)
		else:
				snake_case__	     =student_model_class(UpperCamelCase_			)

		if args.n_gpu > 0:
				student.to(f"""cuda:{args.local_rank}"""			)
		logger.info('Student loaded.'			)

		# TEACHER #
		snake_case__	     =teacher_model_class.from_pretrained(args.teacher_name      ,					output_hidden_states=UpperCamelCase_			)
		if args.n_gpu > 0:
				teacher.to(f"""cuda:{args.local_rank}"""			)
		logger.info(f"""Teacher loaded from {args.teacher_name}."""			)

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(UpperCamelCase_      ,					UpperCamelCase_			)
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(UpperCamelCase_      ,					UpperCamelCase_			)

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0			) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		snake_case__	     =Distiller(
		    params=UpperCamelCase_      ,					dataset=UpperCamelCase_      ,					token_probs=UpperCamelCase_      ,					student=UpperCamelCase_      ,					teacher=UpperCamelCase_			)
		distiller.train()
		logger.info('Let\'s go get some drinks.'			)


if __name__ == "__main__":
			main()









'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 							a__( snake_case__     ):
			a_	: int									=      '''efficientnet'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.5					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**_UpperCAmelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =dropout_rate
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(_UpperCAmelCase						) * 4





class 							a__( snake_case__     ):
			a_	: List[str]									=      version.parse('''1.11'''     )






			@property
			def 						_lowercase  (     self						)							->							Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					    ]						)






			@property
			def 						_lowercase  (     self						)							->							float:
					return 1E-5




